Triplet Spatial Reconstruction Attention-Based Lightweight Ship Component Detection for Intelligent Manufacturing by Bocheng Feng, Zhenqiu Yao, Chuanpu Feng

“…Unlike existing attention mechanisms that focus on either spatial reconstruction or channel attention independently, the proposed TSA integrates triplet parallel processing with spatial feature separation–reconstruction techniques to achieve enhanced target feature representation while significantly reducing parameter count and computational overhead. …”
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Lightweight coal mine conveyor belt foreign object detection based on improved Yolov8n by Jierui Ling, Zhibo Fu, Xinpeng Yuan

“…Abstract To resolve the drawbacks of slow speed, excessive parameters, and high computational demands associated with deep learning-based conveyor belt foreign object detection methods, a lightweight algorithm for detecting foreign objects on conveyors based on an improved Yolov8n model is proposed. …”
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Shuffle window transformer DeepLabV3+: a lightweight convolutional neural network and transformer based hybrid semantic segmentation network by Yane Li, Zhichao Chen, Hongxia Qi, Ming Fan, Lihua Li

“…When the window size is fixed, by integrating window attention (WA) and shuffle WA mechanisms, cross-window global context modeling with linear computational complexity is achieved. Additionally, we enhance the atrous spatial pyramid pooling (ASPP) by incorporating strip pooling to construct a strip ASPP, effectively extracting both regular and irregular multi-scale (MS) features. …”
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A Lightweight Approach to Comprehensive Fabric Anomaly Detection Modeling by Shuqin Cui, Weihong Liu, Min Li

“…In order to solve the problem of high computational resource consumption in fabric anomaly detection, we propose a lightweight network, GH-YOLOx, which integrates ghost convolutions and hierarchical GHNetV2 backbone together to capture both local and global anomaly features. …”
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Classification of Fritillaria thunbergii appearance quality based on machine vision and machine learning technology by DONG Chengye, LI Dongfang, FENG Huaiqu, LONG Sifang, XI Te, ZHOU Qin’an, WANG Jun

“…The tune-up in this study enhanced the detection performance of the model without increasing the number of parameters, computational complexity, or major changes to the original model. …”
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SubsurfaceBreaks v. 1.0: a supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces by M. P. Michalak, C. Gerhards, P. Menzel

“…<p>The study presents a novel approach for fault detection on subsurface geological homoclinal interfaces (slopes) using a supervised learning algorithm and careful input variable (feature) selection. Synthetic faulted slopes are generated using Delaunay triangulation via the Computational Geometry Algorithms Library (CGAL), allowing for adjustments of parameters. …”
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YOLO-MES: An Effective Lightweight Underwater Garbage Detection Scheme for Marine Ecosystems by Chengxu Huang, Wenyuan Zhang, Beitian Zheng, Jiahao Li, Bochen Xie, Ruisi Nan, Zongming Tan, Baohua Tan, Neal N. Xiong

“…This paper also proposes a streamlined Slim-neck design strategy, which effectively reduces the number of parameters in the neck network while maintaining multi-scale feature fusion accuracy. …”
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DC-YOLO: an improved field plant detection algorithm based on YOLOv7-tiny by Wenwen Li, Yun Zhang

“…Finally, we decoupled the detection head to minimize conflicts between features from different tasks. The results show that applying the proposed method to corn and weed datasets, the detection accuracy of the model reaches 95.7% mean Average Precision (mAP@0.5), the computational effort of the model is 13.083 Giga Floating-point Operations (GFLOPs), and the parameter size is 5.223 Millon (M), which is better than the rest of the mainstream light-weight target detection model.…”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…The ADown module dynamically adapts its downsampling strategy according to the feature characteristics, effectively reducing the number of parameters and computational complexity, while enhancing the model's ability to capture crack edges and fine textural details. …”
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Prediction of porosity, hardness and surface roughness in additive manufactured AlSi10Mg samples. by Fatma Alamri, Imad Barsoum, Shrinivas Bojanampati, Maher Maalouf

“…Feature importance analysis on the compiled dataset using ANN revealed that laser power and scan speed are the most important features affecting relative density (e.g., porosity) and hardness, while scan speed and layer thickness significantly impact the surface roughness of the parts. …”
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GSF-YOLOv8: A Novel Approach for Fire Detection Using Gather-Distribute Mechanism and SimAM Attention by Caixiong Li, Dali Wu, Xing Zhang, Peng Wu

“…To address the current challenges in fire detection algorithms, including insufficient feature extraction, high computational complexity, limited deployment on resource-constrained devices, missed detections, false detections, and low accuracy, we developed a high-precision algorithm named GSF-YOLOv8. …”
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Intelligent deep learning architecture for precision vegetable disease detection advancing agricultural new quality productive forces by Jun Liu, Xuewei Wang, Qian Chen, Peng Yan, Dugang Guo

“…The Adaptive Detail Enhancement Convolution (ADEConv) module employs dynamic parameter adjustment to preserve fine-grained features while maintaining computational efficiency. …”
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Fluid Equation-Based and Data-Driven Simulation of Special Effects Animation by Yujuan Deng

“…For continuous image sequences, a linear dynamic model algorithm based on pyramidal optical flow is used to track the feature centers of the objects, and the spatial coordinates and motion parameters of the feature points are obtained by reconstructing the motion trajectories. …”
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Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization by Huaitao Shi, Yangyang Li, Peng Zhou, Shenghao Tong, Liang Guo, Baicheng Li

“…The potential well functions are mostly set fixed to reduce computational complexity, and the SR methods with fixed potential well parameters have better performances in stable working conditions. …”
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Research on foreign object intrusion detection in railway tracks based on MSL-YOLO by Hongxia Niu, Dingchao Feng, Tao Hou

“…Specifically, a Multi-scale Shared Convolution Module (MSCM) is designed to replace SPPF, enhancing feature extraction while reducing parameters and computational cost. …”
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TongueNet: a multi-modal fusion and multi-label classification model for traditional Chinese Medicine tongue diagnosis by Lijuan Yang, Lijuan Yang, Qiumei Dong, Da Lin, Xinliang Lü

“…Moreover, TongueNet contains only 32.1 M parameters, significantly reducing computational resource requirements while maintaining high diagnostic performance. …”
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Conformal On-Body Antenna System Integrated with Deep Learning for Non-Invasive Breast Cancer Detection by Marwa H. Sharaf, Manuel Arrebola, Khalid F. A. Hussein, Asmaa E. Farahat, Álvaro F. Vaquero

“…A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. …”
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HPRT-DETR: A High-Precision Real-Time Object Detection Algorithm for Intelligent Driving Vehicles by Xiaona Song, Bin Fan, Haichao Liu, Lijun Wang, Jinxing Niu

“…This integration expands the model’s receptive field and enhances feature extraction without adding learnable parameters or complex computations, effectively minimizing missed detections of small targets. …”
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DeLA: An extremely faster network with decoupled local aggregation for large scale point cloud learning by Weikang Yang, Xinghao Lu, Binjie Chen, Chenlu Lin, Xueye Bao, Weiquan Liu, Yu Zang, Junyu Xu, Cheng Wang

“…Unlike simple pooling, neighborhood aggregation incorporates spatial relationships between points into the feature aggregation process, requiring repeated relationship learning and resulting in substantial computational redundancy. …”
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Smoke Detection Transformer: An Improved Real-Time Detection Transformer Smoke Detection Model for Early Fire Warning by Baoshan Sun, Xin Cheng

“…Considering the limited computational resources of smoke detection devices, Enhanced Channel-wise Partial Convolution (ECPConv) is introduced to reduce the number of parameters and the amount of computation. …”
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